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import os
from copy import deepcopy
from dataclasses import dataclass
from typing import Optional, Tuple

import torch
import torch.nn as nn

from transformers import (
    AutoTokenizer,
    AutoModel,
)
from transformers.utils import ModelOutput


def use_default(value, default):
    """Utility: return value if not None, else default."""
    return value if value is not None else default

# Prompt templates for different models and tasks


__all__ = [
    "C_SCALE", "PROMPT_TEMPLATE",
    "MODEL_BASE",
]

# =================== Constant Values =====================
# Computation scale factor, 1P = 1_000_000_000_000_000. Tensorboard will display the value in PetaFLOPS to avoid
# overflow error when tensorboard logging values.
C_SCALE = 1_000_000_000_000_000

PROMPT_TEMPLATE_ENCODE_IMAGE_JSON = [
    {"role": "system", "content": "You are a helpful assistant. Describe the image by detailing the following aspects: \
        1. The main content and theme of the image. \
        2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects. \
        3. The background environment, light, style and atmosphere."},
    {"role": "user", "content": "{}"}
]

PROMPT_TEMPLATE_ENCODE_VIDEO_JSON = [
    {"role": "system", "content": "You are a helpful assistant. Describe the video by detailing the following aspects: \
        1. The main content and theme of the video. \
        2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects. \
        3. Actions, events, behaviors temporal relationships, physical movement changes of the objects. \
        4. background environment, light, style and atmosphere. \
        5. camera angles, movements, and transitions used in the video."},
    {"role": "user", "content": "{}"}
]

PROMPT_TEMPLATE = {
    "li-dit-encode-image-json": {"template": PROMPT_TEMPLATE_ENCODE_IMAGE_JSON, "crop_start": -1}, # auto-calculate crop_start
    "li-dit-encode-video-json": {"template": PROMPT_TEMPLATE_ENCODE_VIDEO_JSON, "crop_start": -1}, # auto-calculate crop_start
}


MODEL_BASE = os.getenv("MODEL_BASE", "")
TEXT_ENCODER_PATH = {}
TOKENIZER_PATH = {}

PRECISION_TO_TYPE = {
    'fp32': torch.float32,
    'fp16': torch.float16,
    'bf16': torch.bfloat16,
}


def load_text_encoder(
    text_encoder_type,
    text_encoder_precision=None,
    text_encoder_path=None,
    logger=None,
    device=None,
):
    if text_encoder_path is None:
        if text_encoder_type not in TEXT_ENCODER_PATH:
            raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
        text_encoder_path = TEXT_ENCODER_PATH[text_encoder_type]

    text_encoder = AutoModel.from_pretrained(text_encoder_path, low_cpu_mem_usage=True)
    
    if hasattr(text_encoder, 'language_model'):
        text_encoder = text_encoder.language_model
    text_encoder.final_layer_norm = text_encoder.norm
    
    # from_pretrained will ensure that the model is in eval mode.
    if text_encoder_precision is not None:
        text_encoder = text_encoder.to(dtype=PRECISION_TO_TYPE[text_encoder_precision])

    text_encoder.requires_grad_(False)

    if device is not None:
        text_encoder = text_encoder.to(device)

    return text_encoder, text_encoder_path


def load_tokenizer(
    tokenizer_type, tokenizer_path=None, padding_side="right", logger=None
):
    processor = None
    if tokenizer_path is None:
        if tokenizer_type not in TOKENIZER_PATH:
            raise ValueError(f"Unsupported tokenizer type: {tokenizer_type}")
        tokenizer_path = TOKENIZER_PATH[tokenizer_type]

    tokenizer = AutoTokenizer.from_pretrained(
        tokenizer_path, padding_side=padding_side
    )

    return tokenizer, tokenizer_path, processor


@dataclass
class TextEncoderModelOutput(ModelOutput):
    """
    Base class for model's outputs that also contains a pooling of the last hidden states.

    Args:
        hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
        hidden_states_list (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        text_outputs (`list`, *optional*, returned when `return_texts=True` is passed):
            List of decoded texts.
    """

    hidden_state: torch.FloatTensor = None
    attention_mask: Optional[torch.LongTensor] = None
    hidden_states_list: Optional[Tuple[torch.FloatTensor, ...]] = None
    text_outputs: Optional[list] = None
    image_features: Optional[list] = None

class TextEncoder(nn.Module):
    def __init__(
        self,
        text_encoder_type: str,
        max_length: int,
        text_encoder_precision: Optional[str] = None,
        text_encoder_path: Optional[str] = None,
        tokenizer_type: Optional[str] = None,
        tokenizer_path: Optional[str] = None,
        output_key: Optional[str] = None,
        use_attention_mask: bool = True,
        prompt_template: Optional[dict] = None,
        prompt_template_video: Optional[dict] = None,
        hidden_state_skip_layer: Optional[int] = None,
        apply_final_norm: bool = False,
        reproduce: bool = False,
        logger=None,
        device=None,
    ):
        super().__init__()
        self.text_encoder_type = text_encoder_type
        self.max_length = max_length
        self.precision = text_encoder_precision
        self.model_path = text_encoder_path
        self.tokenizer_type = (
            tokenizer_type if tokenizer_type is not None else text_encoder_type
        )
        self.tokenizer_path = (
            tokenizer_path if tokenizer_path is not None else text_encoder_path
        )
        self.use_attention_mask = use_attention_mask
        if prompt_template_video is not None:
            assert (
                use_attention_mask is True
            ), "Attention mask is True required when training videos."
        self.prompt_template = prompt_template
        self.prompt_template_video = prompt_template_video
        self.hidden_state_skip_layer = hidden_state_skip_layer
        self.apply_final_norm = apply_final_norm
        self.reproduce = reproduce
        self.logger = logger

        self.use_template = self.prompt_template is not None
        if self.use_template:
            assert (
                isinstance(self.prompt_template, dict)
                and "template" in self.prompt_template
            ), f"`prompt_template` must be a dictionary with a key 'template', got {self.prompt_template}"
            assert "{}" in str(self.prompt_template["template"]), (
                "`prompt_template['template']` must contain a placeholder `{}` for the input text, "
                f"got {self.prompt_template['template']}"
            )

        self.use_video_template = self.prompt_template_video is not None
        if self.use_video_template:
            if self.prompt_template_video is not None:
                assert (
                    isinstance(self.prompt_template_video, dict)
                    and "template" in self.prompt_template_video
                ), f"`prompt_template_video` must be a dictionary with a key 'template', got {self.prompt_template_video}"
            assert "{}" in str(self.prompt_template_video["template"]), (
                "`prompt_template_video['template']` must contain a placeholder `{}` for the input text, "
                f"got {self.prompt_template_video['template']}"
            )

        if text_encoder_type != "llm":
            raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
        self.output_key = output_key or "last_hidden_state"

        self.model, self.model_path = load_text_encoder(
            text_encoder_type=self.text_encoder_type,
            text_encoder_precision=self.precision,
            text_encoder_path=self.model_path,
            logger=self.logger,
            device=device,
        )

        self.tokenizer, self.tokenizer_path, self.processor = load_tokenizer(
            tokenizer_type=self.tokenizer_type,
            tokenizer_path=self.tokenizer_path,
            padding_side="right",
            logger=self.logger,
        )

        # pre-calculate crop_start for image and video
        if self.use_template and self.prompt_template is not None:
            self.text2tokens("a photo of a cat", data_type="image")
        if self.use_video_template and self.prompt_template_video is not None:
            self.text2tokens("a photo of a cat", data_type="video")

    @property
    def dtype(self):
        return self.model.dtype
    
    @property
    def device(self):
        return self.model.device

    def __repr__(self):
        return f"{self.text_encoder_type} ({self.precision} - {self.model_path})"

    @staticmethod
    def apply_text_to_template(text, template, prevent_empty_text=True):
        """
        Apply text to template.

        Args:
            text (str): Input text.
            template (str or list): Template string or list of chat conversation.
            prevent_empty_text (bool): If Ture, we will prevent the user text from being empty
                by adding a space. Defaults to True.
        """
        if isinstance(template, str):
            # Will send string to tokenizer. Used for llm
            return template.format(text)
        elif isinstance(template, list):
            # For JSON list template format (chat conversation)
            # Create a deep copy to avoid modifying the original template
            template_copy = deepcopy(template)
            for item in template_copy:
                if isinstance(item, dict) and "content" in item:
                    # Replace placeholder with text in the content field
                    item["content"] = item["content"].format(text if text else (" " if prevent_empty_text else ""))
            return template_copy
        else:
            raise TypeError(f"Unsupported template type: {type(template)}")

    def calculate_crop_start(self, tokenized_input):
        """
        Automatically calculate the crop_start position based on identifying user tokens.
        
        Args:
            tokenized_input: The output from the tokenizer containing input_ids
            
        Returns:
            int: The position where the actual prompt content begins (after user markers)
        """
        input_ids = tokenized_input["input_ids"][0].tolist()  # Get the first example's tokens
        
        marker = "<|im_start|>user\n"
            
        # Tokenize just the marker to get its token IDs
        marker_tokens = self.tokenizer(marker, add_special_tokens=False)["input_ids"]
        
        # Find the end position of the marker in the input sequence
        for i in range(len(input_ids) - len(marker_tokens) + 1):
            if input_ids[i:i+len(marker_tokens)] == marker_tokens:
                # Return the position after the marker
                return i + len(marker_tokens)
                
        # If marker not found, try to find based on special tokens
        if hasattr(self.tokenizer, 'special_tokens_map'):
            # Check for user token or any other special token that might indicate user input start
            for token_name, token_value in self.tokenizer.special_tokens_map.items():
                if 'user' in token_name.lower():
                    user_token_id = self.tokenizer.convert_tokens_to_ids(token_value)
                    if user_token_id in input_ids:
                        return input_ids.index(user_token_id) + 1
        
        # Default fallback: return 0 (no cropping)
        return 0

    def text2tokens(self, text, data_type="image", max_length=300):
        """
        Tokenize the input text.

        Args:
            text (str or list): Input text.
        """
        tokenize_input_type = "str"
        if self.use_template or self.use_video_template:
            if data_type == "image":
                prompt_template = self.prompt_template["template"]
                crop_start = self.prompt_template.get("crop_start", -1)
            elif data_type == "video":
                prompt_template = self.prompt_template_video["template"]
                crop_start = self.prompt_template_video.get("crop_start", -1)
            else:
                raise ValueError(f"Unsupported data type: {data_type}")
            if isinstance(text, (list, tuple)):
                text = [
                    self.apply_text_to_template(one_text, prompt_template)
                    for one_text in text
                ]
                if isinstance(text[0], list):
                    tokenize_input_type = "list"
            elif isinstance(text, str):
                text = self.apply_text_to_template(text, prompt_template)
                if isinstance(text, list):
                    tokenize_input_type = "list"
            else:
                raise TypeError(f"Unsupported text type: {type(text)}")
        
            # First pass: tokenize with arbitrary max_length to find crop_start
            if crop_start == -1:
                # Use temporary max_length for the first pass (large enough)
                temp_kwargs = dict(
                    truncation=True,
                    max_length=256,  # Temporary large value
                    padding="max_length",
                    return_tensors="pt",
                )
                
                # First tokenization pass to calculate crop_start
                if tokenize_input_type == "str":
                    temp_tokenized = self.tokenizer(
                        text,
                        return_length=False,
                        return_overflowing_tokens=False,
                        return_attention_mask=True,
                        **temp_kwargs,
                    )
                elif tokenize_input_type == "list":
                    temp_tokenized = self.tokenizer.apply_chat_template(
                        text,
                        add_generation_prompt=True,
                        tokenize=True,
                        return_dict=True,
                        **temp_kwargs,
                    )
                
                # Calculate the crop_start from this first pass
                crop_start = self.calculate_crop_start(temp_tokenized)
                
                # Store the calculated crop_start for future use
                if data_type == "image":
                    self.prompt_template["crop_start"] = crop_start
                else:
                    self.prompt_template_video["crop_start"] = crop_start
        else:
            crop_start = 0
        
        # Second pass: tokenize with the proper max_length using the found crop_start
        kwargs = dict(
            truncation=True,
            max_length=max_length + (crop_start if crop_start > 0 else 0),
            padding="max_length",
            return_tensors="pt",
        )
        
        if tokenize_input_type == "str":
            tokenized_output = self.tokenizer(
                text,
                return_length=False,
                return_overflowing_tokens=False,
                return_attention_mask=True,
                **kwargs,
            )
        elif tokenize_input_type == "list":
            tokenized_output = self.tokenizer.apply_chat_template(
                text,
                add_generation_prompt=True,
                tokenize=True,
                return_dict=True,
                **kwargs,
            )
        else:
            raise ValueError(f"Unsupported tokenize_input_type: {tokenize_input_type}")
                
        return tokenized_output

    def encode(
        self,
        batch_encoding,
        use_attention_mask=None,
        output_hidden_states=False,
        do_sample=None,
        hidden_state_skip_layer=None,
        return_texts=False,
        data_type="image",
        device=None,
        is_uncond=False
    ):
        """
        Args:
            batch_encoding (dict): Batch encoding from tokenizer.
            use_attention_mask (bool): Whether to use attention mask. If None, use self.use_attention_mask.
                Defaults to None.
            output_hidden_states (bool): Whether to output hidden states. If False, return the value of
                self.output_key. If True, return the entire output. If set self.hidden_state_skip_layer,
                output_hidden_states will be set True. Defaults to False.
            do_sample (bool): Whether to sample from the model. Used for Decoder-Only LLMs. Defaults to None.
                When self.produce is False, do_sample is set to True by default.
            hidden_state_skip_layer (int): Number of hidden states to hidden_state_skip_layer. 0 means the last layer.
                If None, self.output_key will be used. Defaults to None.
            return_texts (bool): Whether to return the decoded texts. Defaults to False.
        """
        device = self.model.device if device is None else device
        use_attention_mask = use_default(use_attention_mask, self.use_attention_mask)
        hidden_state_skip_layer = use_default(
            hidden_state_skip_layer, self.hidden_state_skip_layer
        )
        do_sample = use_default(do_sample, not self.reproduce)

        attention_mask = (
            batch_encoding["attention_mask"].to(device) if use_attention_mask else None
        )
        outputs = self.model(
            input_ids=batch_encoding["input_ids"].to(device),
            attention_mask=attention_mask,
            output_hidden_states=output_hidden_states
            or hidden_state_skip_layer is not None,
        )
        if hidden_state_skip_layer is not None:
            last_hidden_state = outputs.hidden_states[-(hidden_state_skip_layer + 1)]
            # Real last hidden state already has layer norm applied. So here we only apply it
            # for intermediate layers.
            if hidden_state_skip_layer > 0 and self.apply_final_norm:
                last_hidden_state = self.model.final_layer_norm(last_hidden_state)
        else:
            last_hidden_state = outputs[self.output_key]

        # Remove hidden states of instruction tokens, only keep prompt tokens.
        if self.use_template:
            if data_type == "image":
                crop_start = self.prompt_template.get("crop_start", 0)
            elif data_type == "video":
                crop_start = self.prompt_template_video.get("crop_start", 0)
            else:
                raise ValueError(f"Unsupported data type: {data_type}")
            if crop_start > 0:
                last_hidden_state = last_hidden_state[:, crop_start:]
                attention_mask = (
                    attention_mask[:, crop_start:] if use_attention_mask else None
                )

        if output_hidden_states:
            return TextEncoderModelOutput(
                last_hidden_state, attention_mask, outputs.hidden_states
            )
        return TextEncoderModelOutput(last_hidden_state, attention_mask)


    def forward(
        self,
        text,
        use_attention_mask=None,
        output_hidden_states=False,
        do_sample=False,
        hidden_state_skip_layer=None,
        return_texts=False,
    ):
        batch_encoding = self.text2tokens(text, max_length=self.max_length)
        return self.encode(
            batch_encoding,
            use_attention_mask=use_attention_mask,
            output_hidden_states=output_hidden_states,
            do_sample=do_sample,
            hidden_state_skip_layer=hidden_state_skip_layer,
            return_texts=return_texts,
        )
